GPT-5.5 vs Claude Opus 4.7 vs Gemini 3.1 Pro: Which to Pay For

Three frontier models launched within six weeks of each other, all priced within shouting distance, all claiming the benchmark crown in different categories. We ran the same task battery across all three—coding, long-form writing, agentic pipelines, and plain old factual accuracy—and the split decision surprised us. Here’s what the numbers actually say, and who should be writing whose check.

The Pricing Table: Where They Actually Sit

GPT-5.5 lands at $5 per million input tokens and $30 per million output tokens. Claude Opus 4.7 runs $5 input / $25 output. Gemini 3.1 Pro costs $12 per million output tokens.

For a development team burning 10 billion output tokens per month, that’s the difference between $250K (Gemini), $300K (Opus), and $300K+ (GPT-5.5). On paper they’re peers. In practice, token efficiency and benchmark performance flip the calculus entirely.

Coding: Where Opus 4.7 Actually Earns Its Premium

SWE-bench tells the story plainly. GPT-5.5 scores 88.7%, Opus 4.7 scores 87.6%—a gap that rounds to nothing. But the kind of problem matters. Opus 4.7 pulls ahead on SWE-Bench Pro (where it scores 64.3% vs GPT-5.5’s 62.1%), the subset that tests multi-file edits, test harness integration, and production-grade PRs.

Here’s the move: if you’re running long-lived agent loops that need to edit code over multiple turns, Opus 4.7’s native MCP integration and lower hallucination rate (we’ll get to that) means fewer failed handoffs. GPT-5.5’s stronger agentic reasoning (82.7% on Terminal-Bench vs Opus’s 69.4%) wins on one-shot tasks and pure reasoning, but in a multi-turn coding session, Opus’s consistency compounds.

For daily driver work, Claude Sonnet 4.6 at $3/$15 per million tokens outperforms both on real codebases, thanks to a 1-million-token context window neither Opus nor GPT-5.5 matches. But if you’re picking between the two flagships for coding, Opus 4.7 is the rational choice.

Long-Form Writing: The Surprise Winner

Opus 4.7’s hallucination rate on long-form factual writing sits at 36%, meaning roughly one in three generated paragraphs contains at least one unfounded claim. GPT-5.5’s rate: 86%.

We tested this ourselves. Same 5,000-word prompt about rare earths supply chains. Opus 4.7 returned two sentences with unsourced detail. GPT-5.5 returned a full paragraph of plausible-sounding fiction, twice. If you’re drafting long-form where factual weight matters—research reports, trend analysis, anything you can’t fully fact-check before publishing—Opus 4.7 is not just cheaper, it’s safer.

If you’re building long-form on top of Opus 4.7, Jasper’s the integration we keep coming back to—here’s our full review. It handles the retrieval layer and source citing that keeps Opus’s output anchored.

Agentic Pipelines and Tool Use: Gemini 3.1 Pro’s Argument

This is where Gemini lands its case. On OSWorld (a benchmark testing real autonomous agent behavior—clicking buttons, navigating UIs, orchestrating multi-step workflows), Gemini 3.1 Pro scores 75% while both Opus 4.7 and GPT-5.5 land in the 60s. For reasoning problems (GPQA Diamond), Gemini 3.1 Pro hits 94.3% vs Opus’s 89.5%.

The catch: Gemini excels when you can parallelize tasks and accept slight factual flexibility. GPT-5.5’s agentic edge shows up in sequential, tightly-coupled workflows where reasoning persistence matters. Opus 4.7 sits between—reliable but not the sharpest on pure reasoning chains.

For cost-sensitive shops running high-volume autonomous agents, Gemini’s $12-per-million output pricing against stronger reasoning scores is the real win. But if your agent needs to trust its tool responses and build on them reliably, GPT-5.5’s 82.7% Terminal-Bench score buys you something Gemini doesn’t: confidence that the next step will use the previous answer correctly.

Hallucination Rates: The Number Nobody’s Putting in Headlines

We have to flag the elephant: GPT-5.5’s long-form hallucination rate of 86% is real, and OpenAI’s not exactly leading with it. That number comes from controlled testing on factual recall—it’s not saying GPT-5.5 always makes things up. But on generation tasks where you can’t post-verify every claim (customer-facing summaries, compliance docs, reports destined for non-experts), that rate is a blocker.

Claude Opus’s 36% rate means you’ll still need review—but at a ratio where human QA is plausible. At 86%, you’re building a whole fact-check layer or accepting significant risk.

What We’d Actually Pay For

Coding agents are where Opus 4.7 earns its rate card. The multi-turn consistency we saw in our own runs compounds in ways the SWE-bench gap doesn’t capture—fewer failed handoffs, cleaner context management across long sessions, and MCP integration that keeps the loop tight. At the same per-input price as GPT-5.5, the output savings and the avoided rework push Opus into the clear for production code work.

Pure reasoning tasks tell a different story. Logic puzzles, STEM problem solving, constraint satisfaction—Gemini 3.1 Pro’s 94.3% GPQA Diamond score isn’t a rounding error. Pair that with the cheapest output pricing in this tier ($12/M vs $25/M and $30/M) and Gemini becomes hard to argue against for anything that isn’t multi-file code or retrieval-grounded long-form.

Long-form factual writing lands with Opus 4.7 by default. The 86% hallucination rate on GPT-5.5 isn’t a flaw you can prompt-engineer around for compliance docs, research reports, or customer-facing copy. The delta between 36% and 86% is the difference between needing a reviewer and needing a fact-check department.

For more on how pricing shapes your LLM stack, check out our pricing comparison of all major models and how to choose between ChatGPT Pro tiers. And if you’re exploring Opus 4.7’s capabilities, our deep dive on token limits and context windows walks through where Opus shines vs where Sonnet suffices.

The Bottom Line

GPT-5.5 is the frontier model most newsworthy—fastest reasoning, strongest agents. But newsworthy and worth-paying-for are different categories. Claude Opus 4.7 wins on the metrics that matter for production code and long-form reliability. Gemini 3.1 Pro wins on reasoning depth and cost-per-task. The frontier now splits three ways, and your choice depends entirely on whether you’re optimizing for reasoning, reliability, or cost. We cry about AI tools so you don’t have to—and right now, the right tool depends on your actual workload, not the press release.